Methods and systems for biometric identification

ABSTRACT

A method for identifying an iris image can include obtaining an iris image of an eye, segmenting the iris image, generating, from the segmented iris image, a normalized iris image, and generating, from the normalized iris image, an iris template. The method can also include generating a modified iris template by extracting a portion of the iris template, comparing the modified iris template with a plurality of previously stored other modified iris templates and matching the modified iris template with one of the plurality of previously stored other modified iris templates. The method can also include generating a modified iris template by extracting a portion of the iris template, comparing the modified iris template with a plurality of previously stored other modified iris templates, and matching the modified iris template with one of the plurality of previously stored other modified iris templates.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. Non-ProvisionalPatent Application No. 11/510,197 filed on Aug. 25, 2006, which claimsthe benefit of U.S. Provisional Application No. 60/711,105, filed Aug.25, 2005, U.S. Provisional Application No. 60/711,106, filed Aug. 25,2005, and U.S. Provisional Application No. 60/711,107, filed Aug. 25,2005. The entire disclosures of U.S. Non-Provisional Patent ApplicationNo. 11/510,197, and U.S. Provisional Application Serial Nos. 60/711,105,60/711,106, and 60/711,107 are incorporated by reference herein.

FIELD OF THE INVENTION

Embodiments of the present invention generally relate to biometricidentification systems and in particular to biometric identificationsystems using iris recognition methods.

BACKGROUND OF THE INVENTION

Iris recognition is one of the most powerful techniques for biometricidentification ever developed. Commercial systems, like those based onan algorithm developed by Daugman, (see U.S. Pat. No. 5,291,560, thecontents of which are hereby incorporated by reference herein) have beenavailable since 1995 and have been used in a variety of practicalapplications.

The basic principles of iris recognition are summarized in FIG. 1. Thesubject iris 10 is illuminated with light from controlled and ambientlight sources 12. The camera 14 and the controlled illumination 16 areat some defined standoff distance 18, 20, from the subject. The cameraand lens 14 (possibly filtered) acquires an image that is then capturedby a computer 22. The iris image 24 is then segmented, normalized and aniris template (commonly called an iris code) 28 is generated.Segmentation identifies the boundaries of the pupil and iris.Normalization remaps the iris region between the pupil and the sclera(the white of the eye) into a form convenient for template generationand removes the effects of pupil dilation using a suitable model. Thetemplate is then matched against an existing database 30 of previouslyenrolled irises; a match against the database indicates the current irisis the same iris used to create the template in the database.

However, prior iris recognition techniques suffer from severaldifficulties for some applications. One difficulty is that when used insystems with large (e.g., 500,000) people in the database, such systemsrequire a large number of servers to accommodate the large number ofpeople in the databases.

Another difficulty is that prior techniques have been designed andoptimized for applications in which the false match rate is of paramountimportance—neglecting applications in which other factors are moreimportant than the false match rate or in which other engineeringtradeoffs should be considered. In addition, these techniques expect areasonably high quality image. There are applications where one wouldaccept a higher false match rate in return for the ability to use lowerquality images. Forensic applications are one example. Acquisition ofimages for iris recognition in less constrained environments is anotherexample.

Acquisition of high quality iris images is difficult because the humaniris is a small target (−1 cm diameter), with relatively low albedo(˜0.15), in the near IR. Existing iris recognition algorithms recommenda resolution of the order of 200 pixels across the iris. Hence,acquisition of iris images of sufficient quality for iris recognition ischallenging, particularly from a distance. Current commerciallyavailable iris cameras require substantial cooperation on the part ofthe subject. Two simple metrics for the required degree of cooperationare the capture volume and the residence time in the capture volume. Thecapture volume is the three dimensional volume into which an eye must beplaced and held for some period of time (the residence time) in orderfor the system to reliably capture an iris image of sufficient qualityfor iris recognition. For ease of use, the user would want the spatialextent of the capture volume to be as large as possible and theresidence time to be as small as possible.

A related issue is the standoff distance, the distance between thesubject and the iris image acquisition system. Existing systems requirereasonably close proximity, in some cases an ‘in your face’ proximity.Existing iris recognition algorithms are generally good enough for mostapplications in which the subject is sufficiently cooperative. Achallenge resides in reducing constraints on the subject so irisrecognition is easier to use.

In scenarios in which iris recognition needs to be deployed in lessconstrained environments, we can reasonably expect that the acquirediris images will be of lower quality than those in highly constrainedenvironments. Hence, there is a need for algorithms that can work withlower quality images, even at the possible expense of higher false matchand/or false non-match rates.

Current iris recognition algorithms search a template database by bruteforce. The algorithms used are very efficient, but they conduct thesearch by systematically testing against every template in the databaseuntil a match is found. For large databases that are subject to highinterrogation rates this consumes many CPU cycles and requiresdeployment of large collections of computers to provide acceptableresponse rates. Thus, there is a need for a method that can improve thesearch rate over existing algorithms and one that will decrease theequipment costs for the database searches. This will become particularlyimportant as high throughput iris recognition systems become widelydeployed.

Thus, there are multiple reasons that we need improvements to existingiris recognition methods, including alternatives to the existingmethods.

SUMMARY OF THE INVENTION

Embodiments of the present invention address these and other needs andgenerally relate to methods for improving the acquisition of irisimages, improving methods of generating iris templates and for improvingmethods for searching template databases.

Embodiments of the present invention generally relate to methods forimproving performance of an iris recognition system. The methods caninclude using iris image registration and averaging steps to improveperformance. In one embodiment, the iris image is averaged. In anotherembodiment, the segmented iris image is averaged. In yet anotherembodiment, the normalized iris image is averaged. In anotherembodiment, the biometric template is averaged. As used herein, the term‘averaged’ denotes a combining of two or more entities, such as iristemplates, based on a set of rules based on information that can becontained both within and external to the entity, or iris template. Theterm average, as used herein, is not restricted to the narrower conceptof an arithmetic mean. The purpose of the averaging is to create anentity which is better than any one of the individual entities that havebeen averaged.

Embodiments of the present invention include methods for irisrecognition in which false match rates are balanced against otherperformance parameters.

In one embodiment, there is provided an iris recognition method forcapturing iris images that provide increased capture volume, decreasedacquisition time, increased standoff and the capability of acquisitionof iris images from moving subjects.

Embodiments of a method for identifying an iris image can includeobtaining an iris image of an eye, segmenting the iris image,generating, from the segmented iris image, a normalized iris image, andgenerating, from the normalized iris image, an iris template. The methodcan also include generating a modified iris template by extracting aportion of the iris template, comparing the modified iris template witha plurality of previously stored other modified iris templates andmatching the modified iris template with one of the plurality ofpreviously stored other modified iris templates.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will be more readily understoodfrom the detailed description of exemplary embodiments presented belowconsidered in conjunction with the attached drawings, of which:

FIG. 1 is a schematic of an iris recognition system;

FIG. 2A depicts an iris image with segmentation indicated;

FIG. 2B depicts an iris image that has been normalized.

FIG. 3A depicts an original iris image;

FIG. 3B depicts the iris image of FIG. 3A with segmentation indicated;

FIG. 3C depicts a normalized image of the iris image of FIG. 3B; and

FIG. 4 is a graphical display of two iris codes from the same eye andthe difference between them.

It is to be understood that the attached drawings are for purposes ofillustrating the concepts of the invention and may not be to scale.

DETAILED DESCRIPTION OF THE INVENTION

As described above with reference to FIG. 1, a captured iris image isthen segmented, normalized and an iris template (commonly called an iriscode) is generated. The template is then matched against an existingdatabase of previously enrolled irises; a match against the databaseindicates the current iris is the same iris used to create the templatethat is in the database.

Segmentation identifies the boundaries of the pupil and iris.Normalization remaps the iris region between the pupil and the sclera(the white of the eye) using a transform that removes the pupil sizevariation. FIGS. 2A and 2B are an example of a polar transformationdescribed in U.S. Pat. No. 5,291,560 (issued to Daugman), as is known tothose of skill in the art.

FIG. 2A depicts an iris image with segmentation 200 indicated. FIG. 2Bdepicts a normalized image. For this example, the normalized image hasincreasing radius 240 from top to bottom and increasing angle from leftto right, as is known to those of skill in the art. The horizontalstreaks on the segmented image are an artifact of the specularityreduction algorithm used for this example.

An example of an iris code generated by performing a dot product betweencomplex Gabor wavelets and the normalized image is herein described. Thephase angles of resulting complex dot products are then quantized to 2bits and assembled into a bit array that comprises (with possiblyadditional information) an iris template.

With reference to FIG. 4, iris template 400 contains a bit pattern oflight and dark bits 402, 404 that indicate the presence and location ofiris contours. The mask 410 also contains a light and dark bit pattern411, 412 indicating locations in the template 400 of relative high andlow confidence of accuracy, as is known to those of skill in the art.

The comparison step computes the Hamming distance between the bit arrayof one template and that of another and compares that distance to apre-determined threshold. The Hamming distance is the number of bitsthat differ between the two bit arrays. The fractional Hamming distanceis the fraction of the bits that differ between the two templates. Theterm “Hamming distance” is conventionally used in this context to denotethe fractional Hamming distance—and this convention is adopted for thepurposes of this description. It has been has shown that Hammingdistances less than 0.33 are statistically unlikely for templates thatarise from different irises. Some matching algorithms adjust the rawHamming distance based upon the number of bits available in thetemplates and the size of the enrolled database to produce a modifiedHamming distance, as is known to those of skill in the art.

In accordance with embodiments of the present invention, there areprovided alternative methods for iris template generation, for templatecomparison and methods for modification of conventional iris templatesthat enable improvements in the performance of iris recognition systems.The alternative and modified templates and comparison methods can beuseful in the construction of reference databases and in preparation oftemplates to be compared with the reference database.

In accordance with embodiments of the present invention, there isprovided a method for using a lower reliability method to search theiris template database for likely matches that can be subsequentlyconfirmed with a higher reliability method, such as, for example, amethod practiced by Daugman. If the lower reliability method generatesfalse matches at a rate PFM the computation cost of the new proposedmethod is:

CCnew=P _(FM) NO _(H) +NO _(L) =N(P _(FM) O _(H) +O _(L))

where O_(H) is the computational cost for a single comparison of thehigh reliability method and O_(L) is the computational cost for a singlecomparison of the low reliability method.—The computational cost usingonly the high reliability method is simply:

CC_(H)=NO_(H)

Hence the ratio of the costs is expressed as:

CC _(new) /CC _(H)=(P _(FM) O _(H) +O _(L))/O _(H) =P _(FM+) O _(L) /O_(H)

Clearly, if O_(L)<<O_(H) and P_(FM)<<1, the computational cost of thenew method can be much smaller than applying the high reliability methodalone.

Several embodiments of lower reliability methods involve extraction of aModified Iris Template that contains a subset of the informationcontained in the original iris template. Matching this modified iristemplate against a correspondingly modified stored iris template willyield a partial measure of similarity that can provide lower reliabilityidentity information. Exemplary embodiments of such partial templatemethods that could be used with templates of the form used inDaugman-like methods include, but are not limited to: 1) row extractionand 2) column extraction. Another embodiment of lower reliability matchmethods involves an indexing process. In this embodiment an indexibleiris template with reduced degrees of freedom is extracted using errorcorrecting code techniques.

Another embodiment of lower reliability match methods involves binningiris templates based on a similarity measure or characteristic metricthat allows grouping templates of similar structure (i.e., binningmethods).

Row Extraction

According to an embodiment of the present invention, a new templateconsisting of the data from a single row of the conventional template isconstructed. If the template has N rows, comparison of such modifiedtemplates will be less computationally expensive by a factor of 1/N. Asan alternative to extracting a single row, the rows could be combinedinto a single row by a generalized averaging technique, for example, aplurality voting or super-majority voting technique.

One can estimate the probability of obtaining a false match using thelow reliability method (P_(FM)) in the following way. As suggested byDaugman, model the matching process using a binomial distribution withp=q=0.5. It has been shown that the number of degrees of freedom in theiris is of the order of n=250. Approximate the binomial distribution ofnormalized Hamming distances (fraction of bits set) by a Gaussian ofmean 0.5 and standard deviation 0.5/√{square root over (n)}. The nominalcutoff for recognition in existing systems is a Hamming distance of0.3—which differs from the mean by 0.2. For n=250, the standarddeviation, is ˜0.032, so the difference is approximately 6 standarddeviations. The probability of an event that is 6 σ or larger isapproximately 1:10⁹. If one reduces the effective n by reducing thenumber of bits that are analyzed, one can increase the probability ofgetting a match between two unrelated templates.

If the reduction is 8 fold, the standard deviation will beσ=0.5/√{square root over ((250/8))} or about 0.09—and the cutoff is now2.2 σ from the mean. The probability of a 2.2 σ or larger deviation fromthe mean is 1.5%.

As such, the following expression may be derived:

CC _(new)=(0.125+0.015)CC _(H)=0.14CC _(H)

This is a factor of approximately 7 times faster than the conventionalapproach. According to an embodiment of the present invention, improvedresults are achieved by this method because the bits in the iris codeare correlated along the vertical directions of the graphical display,corresponding to radial correlations. Hence, taking out a single rowfrom a template of N rows captures more than 1/N of the degrees offreedom in the code.

The Gaussian approximation used here has larger tails than thebinomial—this inflates the PFM estimate and thereby reduces theestimated improvement. According to this particular approach, themaximum improvement is a factor of eight. For example, a backend serverof an iris recognition system requiring $1M worth of hardware using onlythe conventional high reliability method, may realize a cost reductionon the order of $125K using the method described above.

Column Extraction

In this embodiment, there can be provided a step to decimate columns,keeping a constant angular distance between the remaining columns so onecan do the barrel shift described above. This embodiment of the methodis less advantageous than the row extraction because it decreases theangular resolution of the barrel shift and does not make use of thecorrelation between code bits along the columns.

Index Methods

A typical iris template has of the order of 250 degrees of freedom. Onecan use some fraction of those degrees of freedom to generate an errorcorrected reduced iris template of fewer bits at enrollment and storethe error corrected value as an index that can be used as a hash tablelookup into the database of iris templates. The iris template generatedduring recognition will differ from the enrollment. However, if theerror correction works—one can still recover the template index.

This embodiment may use the recovered index to pick the correspondingiris template from the database. With a sorted lookup table this issimply several memory access and compare operations. In the next step ofthis embodiment, the full iris templates are compared. When the indexworks, one saves a factor of approximately N (database size). If theindex fails, the method falls back to any of the methods alreadydescribed. If P_(IF) is the probability that the index fails, thespeedup of the index method is a factor [(1/N)+P_(IF)] over that of thefall back method alone.

Binning Methods

In accordance with an embodiment, the binning method generates acharacteristic metric from the enrollment iris templates that can beused to partition the database of iris codes into R subsets that can besubsequently selected on the basis of the computation of thecharacteristic metric for an identification template. One can use any ofa large variety of characteristics of the iris code.

As an example, in accordance with an embodiment of the presentinvention, one step could compute the energy spectrum of the enrollmentiris templates and select the L largest peaks in the spectrum. Anotherstep could assemble the peak locations into L tables that each share anindex with the table of full iris template and sort the L tables.

In addition, this embodiment may include a step of comparing anidentification template against the database, which allows the method tocompute its peak locations and do full Hamming distance comparisons ofthe identification template against all the enrollment templates thathave a peak in one of the L tables that is within some delta of thepeaks of the identification template. The size of the delta and therange of the peaks location in this embodiment will define R—though thedeltas could be non-uniform.

When the binning works, this embodiment of the method saves a factor ofapproximately R/L. If the binning fails, the steps could fall back toany of the methods already described.

In implementing one of these reduced size template approaches, in oneembodiment, the system generates templates for the proposed search atthe substantially the same time as the system generates the fulltemplates and links them to the full templates in a database. Thereduced size templates may take up less memory, memory use reduction canenable maintenance of the entire database in memory, reducing the needfor disk paging, another speed improvement over and above the simpleanalysis above.

Embodiments of the present invention include methods for improvingcapture of iris images to provide increased capture volume, decreasedacquisition time, increased standoff and the capability of acquisitionof iris images from moving subjects. In one embodiment, multiple imagesare combined to generate a template that is improved as compared to atemplate obtained from any of the individual images. According to anembodiment of the present invention, this can be achieved by generalizedaveraging of the one or more of following items which are generated atvarious stages of the template generation process: 1) the iris image; 2)the segmented iris image; 3) the normalized iris image; and 4) thebiometric template.

In addition, in one embodiment, information from the later stages of thetemplate generation process is used to guide averaging at the earlierstages of the process. The stages are illustrated in FIGS. 3A-C. Themask bits in the template indicate those regions of the template—and thecorresponding locations in the images—where the image was of sufficientquality to get good code bits.

FIG. 3A shows an original iris image 300. FIG. 3B is an iris image withsegmentation indicated 310. The horizontal streaks on the segmentedimage are an artifact of the specularity reduction algorithm used forthis example. FIG. 3C is a normalized image 320. For this example, thenormalized image (FIG. 3C) has increasing radius from top to bottom andincreasing angle from left to right

Averaging Iris Templates

According to an embodiment of the present invention, methods areprovided wherein two templates are compared by computing the Hammingdistance between the code portions of the template as a function ofbarrel shift along the horizontal (angular) axis of the templates. Onlythe bits flagged as good in both masks are included in the comparison.The barrel shift takes into account angular rotation of the eye betweenthe acquisitions of the images for the two templates. For example, atypical template includes 2048 code bits and an equal number of maskbits. As such, for good quality iris images approximately 1500 of themask bits are set; hence, there are approximately 500 bits in the codesections that are flagged as not valid.

FIG. 4 illustrates a comparison 450 of the two codes. At the barrelshift that minimizes the Hamming distance, there are 374 bits (452) inthe difference between the masks. Assuming the mask bits are an accuraterepresentation of the quality of the code bits, one can improve thenumber of valid bits in a new template for this eye by performing thefollowing steps: 1.) barrel shifting one of the templates to align withthe other using Hamming distance as the criterion for best shift; 2)creating a new blank template with all mask bits and code bits turnedoff; 3.) for each location in the pair of original, aligning thetemplates and checking the mask bits.

If both mask bits are set, then check the code bits. If theyagree/match, the corresponding code bit of the new template may be setto that value and the corresponding mask bit of the new template may beset. If the code bits do not agree, the corresponding mask bit of thenew template may be cleared, since the value of the code bit is notknown.

If neither mask bit is set, no action is taken, and the mask and codebits of the new template are cleared because no information for thiscode bit is known.

If one mask bit is set, the corresponding code bit from the templatethat has the mask bit set is selected and the corresponding bit of thenew template is set to the value of the code bit in the template withthe valid mask bit.

In accordance with another embodiment, if there are more than twotemplates, the system can 1.) align all of templates; and 2.) for eachbit location, take a vote among all of the templates for which the maskbit of that location is set, and set the corresponding bit of the newtemplate to the value determined by the outcome of the vote. Further,the mask bit of the new template is set. The vote criteria can be varieddepending on applications. For some applications a simple plurality maybe sufficient, for others a super-majority or some other more complexvote criteria may be optimal. If the vote is not decisive, for examplethe voting does not provide a required super majority for either bitstate, the mask bit would be cleared to indicate that that bit is notreliable.

If, for some bit location, none of the templates has a correspondingmask bit set, the mask and code bits of the new template should becleared.

In accordance with an embodiment, the templates are aligned to thetemplate with the most valid bits. An optimization of the alignmentprocedures is also contemplated by the present invention. One possibleoptimization is to average the two best templates using the two templatemethod described above, then to incrementally add in the othertemplates, one by one to the averaged template.

Averaging Initial Iris Images

One embodiment of the present invention relating to the averaging ofinitial iris images is to include proper alignment and scale. In thecourse of generating the iris templates, the centers and radii of thepupil and iris may be computed. In the course of a comparison of iristemplates, information about the relative rotation of the iris isgenerated. In another embodiment having two images, this information canbe used as follows:

-   -   1. From the radii computed for the two images, compute a scaling        factor and scale the second image so its radii match those of        the first;    -   2. From the centers information of the two images, compute x and        y offsets and shift the images in x and y to match the centers;    -   3. From the barrel shift computed from the templates of the two        images, compute a rotation and rotate the second image about the        pupil center to align it with the first; and/or    -   4. Average the aligned images.

In an embodiment having more than two images, align them all to oneimage and average. In another embodiment, the user would want to pickthe images with the most mask bits set as the alignment image. Theaveraged image can then be used to generate an enhanced template.

Averaging Segmented Iris Images

With access to the segmented iris images, in accordance with anotherembodiment, one can perform an initial alignment as described in theprevious section and then employ correlation (or other) based imagealignment techniques to refine the alignment between the two images—onlyutilizing portions of the image associated with the iris. The averagedimage can then be used to generate an enhanced template.

Averaging Normalized Iris Images

With access to the normalized images, in accordance with anotherembodiment, one can use correlation (or other) based image alignmenttechniques to align the normalized image along the angular (horizontal)axis and then average the normalized images. With access to thenormalized images, one can use the barrel shift information from thetemplate comparison to align the normalized image along the angular(horizontal) axis and then average the normalized images. Alternatively,one can align the normalized images using the barrel shift informationand then refine the alignment using correlation (or other) based imagealignment techniques. Enhanced iris templates generated using thistechnique (including any of the exemplary embodiments) can be used inplace of the standard iris templates, known in the art, both astemplates to be identified and templates to be stored in a referencedatabase.

The embodiment of using information from various stages of the processto assist in alignment and averaging at other stages of the process isan important aspect of embodiments of the present invention.

Image Transformation and Normalization

Analysis of an annular object such as a human iris can be facilitated bytransformation of the image in rectangular co-ordinates to an image inpolar coordinates as shown in FIGS. 2A and 2B. In one embodiment, thereis provided a method for transforming images of the eye taken at nearnormal incidence using a rectangular to polar transformation.

In another embodiment, in situations in which a polar transformation isnot the best model, a transformation into an alternative co-ordinatesystem may be used. The present invention contemplates biometricrecognition for all beings having irises. Tracking the identity oflivestock, zoo animals, pets and racing animals are potentialapplications of iris recognition. Some non-human species have eyes withirises or pupils that are non-circular. In these cases, a transformationinto an alternative co-ordinate system may be preferable.

For example, the pupil of a cat is not circularly symmetric. The catiris is more similar to a slit than a circular hole. Hence, an ellipseis a better model for the shape of the pupil than is a circle and a formof elliptical coordinates as described in “Mathematical Methods forPhysicists,” by G. Arfken, (Academic Press, NY), may be a bettercoordinate system. Depending on the details of the iris and pupilstructure, other classic coordinate systems described in Arken andreferences cited therein may be useful. More complicated non-classiccoordinate systems may also be useful. For example, we may use aphysiological/mechanical model of pupil dilation to construct aspecialized coordinate system in which the pupil dilation can be easilynormalized. In some cases, we may model pupil dilation using elastictheory, in such cases, the Schwarz-Christoffel transformation andrelated transformations from complex variable theory may be used.

When an image of the eye is captured from an angle relative to the opticaxis of the eye, the foreshortening effect changes the apparent shape ofthe iris—It is no longer circular. In this case, a transformation intoan alternative co-ordinate system may be preferable. One embodimentincludes a method for transformation from the rectangular co-ordinatesystem of the image to another rectangular co-ordinate system that takesinto account the projection and a subsequent transformation to acoordinate system appropriate for the eye structure. After thecoordinate transformation(s), a further step may include applying othertransformations to the image.

For example, there may be a step to histogram equalize or gamma correctthe image. The result of the coordinate transformation(s) and whateverother transformations applied is a normalized image ready for subsequentanalysis. In an alternative embodiment, the method may include anevaluation of the original image or the normalized image to determineparts of the image not suitable for analysis for reasons such asspecularities or occlusion.

Analysis of the Normalized Image

There are many embodiments of methods in accordance with the presentinvention for analysis of the transformed image. Here are a fewexamples: Direct Normalized Correlation of Normalized Images:

Normalized images can be directly compared by correlation analysis.Given two normalized images, A and B, subtract off their respectivemeans and then compute the following formula:

C=A•B/√(A•A*B•B)

as a function of barrel shift along the polar axis of the normalizedimage and select the maximum. In this formula, • indicates a dotproduct. The maximum C is the normalized correlation for the images andis a direct measure of similarity of the images. This process alsodetermines the degree of rotation between the two images—it is simplythe barrel shift at the maximum of C. This process can be reasonablyfast because modern image processing boards often have specializedcircuits to carry out this operation at high speed. For a normalizedimage N pixels high and M pixels wide, the computational cost is of theorder of NM²; note the polar nature of the co-ordinate system relievesdifficulties at the horizontal edges of the image.

If meta-data are included for the image that indicates some pixels inthe image are not suitable for analysis, those pixels can be excludedfrom the analysis. Pixels might be excluded because they are part of aspecularity in the iris or an occlusion of the iris.

The direct correlation method is presented first because it is aprototype for the approaches that follow. In the direct correlationmethod, there is a comparison of images rather than some more compactrepresentation of the images. Compact representations have advantagesfor speed of comparison and size of storage for the templates. The morecompact representations are divided into phase like, amplitude like andother, with sub divisions of local and global.

Extraction of Phase Like Global Measures;

In this embodiment, the B image in the direct correlation example isreplaced with two images; one has sine like character, the other hascosine like character. Csine and Ccosine are computed as a function ofbarrel shift (excluding unsuitable pixels as discussed above). For eachbarrel shift, an angle σ is computed from the four quadrant arc-cosine:acos(Csine, Ccosine). Function σ (barrel shift) is generated and isperiodic in barrel shift, that is representative of the image A, andwhich can be compared with σ's extracted from other normalized irisimages to determine similarity of the normalized iris images.

Exemplary comparison methods suitable for use with present inventioninclude, but are not limited to: 1) normalized correlation of the σ's;and 2) quantization of the σ's and comparison of the resulting bitstreams via Hamming distance.

Extraction of Phase Like Local Measures

In this embodiment of the present invention, the B image is replaced inthe direct correlation example with a collection of 2N images; composedof image pairs one has sine like character the other has cosine likecharacter. Each of the N pairs is designed to interrogate a specifiedregion of the A image. Each of the specified regions may be an arbitraryregion or arbitrary collection of arbitrary sub-regions. Csine(i) andCcosine(i) are computed for each of the N pairs; i is the location index(excluding unsuitable pixels as discussed above). For each pair, anangle σ i is computed from the four quadrant arc-cosine: acos(Csine,Ccosine). A function σ (l) is calculated that is representative of theimage A, and which can be compared with σ (i)'s extracted from othernormalized iris images to determine similarity of the normalized irisimages. The number of pixels included for each “i” may be kept track ofand locations that do not have sufficient support or fail some othercriteria for quality are excluded.

Comparison may be more complicated than in the global measure casedepending on the symmetry of the N pairs. If the N pairs are generatedby successive barrel shifts of the first pair, the following exemplaryprocesses may be used in accordance with the present invention: 1)normalized correlation of the σ's; and 2) quantization of the σ's andcomparison of the resulting bit streams via Hamming distance with barrelshifts of the bit streams.

Examples of regions for the first pair of a set of N barrel shiftedpairs which may be used in accordance with the present inventioninclude, but are not limited to: 1) a single vertical (radial) region;2) a group of isolated regions arranged vertically (radially); 3) asingle diagonal (spiral) region; and 4) a group of isolated regionsarranged diagonally (spirally).

Generation of regions with symmetries other than the barrel shiftsymmetry can provide other means for simplifying the comparison.

Extraction of Amplitude Like Local or Global Measures

In an alternative embodiment, the sine-like and cosine-like image pairscan be replaced in the analyses above with single images and process theiris images in the same way except use the correlation, C, rather thanthe acos(Csine, Ccosine).

Mixed Modes

In an alternative embodiment, these techniques can be combined in avariety of ways including, but not limited to: 1) using an amplitude orphase like local or global measure that is tuned for extraction of thebarrel shift needed to align two normalized images to align a pair ofnormalized images and then perform a direct correlation analysis of theimages; 2) using a low resolution, high false match rate local orglobal, amplitude or phase like measure to select candidates for directcorrelation analysis of the normalized images; and/or 3) using a lowresolution, high false match rate local or global, amplitude or phaselike measure to select candidates and define the barrel shift forangular registration of the images. Amplitude or phase like localmeasures can then be computed at a collection of sites on any arbitrarymatrix imposed on the registered normalized images.

One having ordinary skill in the art will appreciate that alternativealignment methods may also be included. For example, two normalizedimages can be aligned using FFT techniques—transform both images viaFFT, take the product, normalize appropriately, transform back and thenfind the peak in the resulting cross correlation function.

It is to be understood that the exemplary embodiments are merelyillustrative of the invention and that many variations of theabove-described embodiments can be devised by one skilled in the artwithout departing from the scope of the invention. It is thereforeintended that all such variations be included within the scope of thefollowing claims and their equivalents.

1. A method for identifying an iris image, the method comprising thesteps of: obtaining an iris image of an eye; generating, from the irisimage, an iris template; generating at least one characteristic metricfrom at least one of the iris template, the iris image and anintermediate product of a process of generating the iris template;assigning the iris template to one of a plurality of categories based onthe at least one characteristic metric; and matching the iris templatewith one of a plurality of other iris templates stored in the assignedcategory.
 2. The method of claim 1, wherein the at least onecharacteristic metric is derived from the energy spectrum of the iristemplate.
 3. A method for building an iris template database, the methodcomprising the steps of: obtaining a plurality iris images of eyes;generating, from each iris image, an iris template; generating at leastone characteristic metric from at least one of the iris template, irisimage, and an intermediate product of a process of generating the iristemplate; assigning each iris template to one of a plurality ofcategories based on the at least one characteristic metric; and storingeach iris template in an data structure searchable based on thecategory.
 4. The method of claim 3, wherein the at least onecharacteristic metric is derived from the energy spectrum of the iristemplate.
 5. A method for identifying an iris image, the methodcomprising the steps of: obtaining an iris image of an eye; generating,from the iris image, an original iris template; generating, from theoriginal iris template, an error-corrected reduced iris template;comparing the error-corrected iris template with a plurality ofpreviously stored other error-corrected reduced iris templates to selectat least one candidate for full unmodified template matching with theoriginal template; and comparing the original iris template with thecorresponding other previously stored original iris templates of theselected at least one candidate.
 6. A method for building an iristemplate database, the method comprising the steps of: obtaining aplurality iris images of eyes; generating, from each iris image, an iristemplate; generating an error corrected reduced iris template from eachiris template; storing each reduced iris template in an indexed datastructure allowing efficient access based on the content of the errorcorrected reduced iris template; and associating with each entry in thisdata structure the corresponding original iris template.
 7. A method foridentifying an iris image, the method comprising the steps of: obtainingan iris image of an eye; segmenting the iris image; establishing anon-polar coordinate system on the segmented iris image wherein thenon-polar coordinate system models dilation of a pupil of the eye;defining a plurality of regions within the iris image; analyzing theiris to generate an iris code; and comparing the iris code with apreviously generated reference iris code within the plurality of regionsto determine a measure of similarity between the iris code and thereference iris code.
 8. The method of claim 7, wherein the non-polarcoordinate system is one of a torroidal coordinate system, and anon-classical complex coordinate system.
 9. The method of claim 8,wherein the step establishing a non-polar coordinate system furthercomprises steps of: applying a coordinate transformation to thesegmented iris image to produce modified segmented iris image inrectangular coordinates; and applying an additional transformation toestablish the non-polar coordinate system.